Triple
T15959347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tony Kushner |
E387015
|
entity |
| Predicate | wroteScreenplayFor |
P15305
|
FINISHED |
| Object | Munich |
E854833
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Munich | Statement: [Tony Kushner, wroteScreenplayFor, Munich]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Munich Context triple: [Tony Kushner, wroteScreenplayFor, Munich]
-
A.
Munich
chosen
"Munich" is a 2005 historical drama thriller film directed by Steven Spielberg that depicts the covert Israeli response to the 1972 Munich Olympics massacre.
-
B.
Munich
Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
-
C.
Munychia
Munychia was a fortified hill and harbor district of ancient Athens, in Piraeus, known as the site where the oligarchic regime of the Thirty Tyrants was overthrown.
-
D.
Leverkusen
Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
-
E.
Regensburg
Regensburg is a historic city in southeastern Germany known for its well-preserved medieval old town on the Danube River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156fe82d081908b5d41bc5a709de2 |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf16b1b881909768d18b889260da |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:53 a.m.